Image processing apparatus, image processing method, and program

ABSTRACT

An image processing apparatus includes: a first layer summary data generation section; a second layer summary data generation section; a first search section; and a second search section.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an image processing apparatus, an imageprocessing method, and a program. More particularly, the inventionrelates to an image processing apparatus, an image processing method,and a program for easily searching and managing numerous moving imagecontents.

2. Description of the Related Art

The technology for managing a large number of moving image contents hasbeen gaining widespread acceptance.

Huge amounts of moving image contents continue to be broadcasted andrecorded every day by broadcasting stations in particular. Theever-growing quantities of moving image contents accumulateuninterruptedly. It is thus extremely difficult to search the numerousmoving image contents for specific contents.

One technique for bypassing the problems above (see Japanese PatentLaid-Open No. 2001-134589 (hereinafter referred to as Patent Document1)) involves making comparisons between the metadata of some of therepresentative images included in the moving image contents targeted forsearch on the one hand, and the metadata of the moving image contentsalready registered and accumulated on the other hand, in order to searchfor and retrieve similar moving image contents.

SUMMARY OF THE INVENTION

Where there exist overlapping moving image contents including imagesmade of the same metadata, the technique disclosed by the PatentDocument 1 demands searching through the result of search by use ofanother representative moving image. Also, given the huge quantities ofmoving image contents including numerous similar images, unless asingularly characteristic representative moving image content is usedfor search, it is increasingly difficult to isolate the desired movingimage content out of the target moving image contents targeted forsearch, as the amount of the moving image contents being managed growsever larger. As a result, searching for a desired moving image contentmay demand expending more and more time and effort than ever before.

The present embodiment has been made in view of the above circumstancesand provides inventive arrangements for easily managing huge amounts ofmoving image contents and for easily searching the large quantities ofmoving image contents being managed for a target moving image content tobe retrieved.

In carrying out the present invention and according to one embodimentthereof, there is provided an image processing apparatus including: afirst layer summary data generation section configured such that out ofimages extracted with a first frequency from the images making up amoving image content, the first layer summary data generation sectiongenerates first layer summary data of a first size before registeringthe first layer summary data to a database; a second layer summary datageneration section configured such that out of images extracted with asecond frequency higher than the first frequency from the images makingup the moving image content, the second layer summary data generationsection generates second layer summary data of a second size smallerthan the first size; a first search section configured such that basedon the first layer summary data generated by the first layer summarydata generation section, the first search section searches the databasefor a corresponding moving image content; and a second search sectionconfigured such that based on the second layer summary data generated bythe second layer summary data generation section, the second searchsection searches the moving image contents retrieved by the first searchsection for a corresponding moving image content.

Preferably, the first layer summary data may be made up of one or acombination of a pixel value, a brightness value, an activity, an audiovolume, and an average of amplitudes within a predetermined audiofrequency band regarding each of a plurality of partitioned regionsconstituting each of the images which are part of the moving imagecontents and which are extracted therefrom with the first frequency, thefirst layer summary data having the first size; and the second layersummary data may be made up of at least one or a combination of a pixelvalue, a brightness value, an activity, an audio volume, and an averageof amplitudes within a predetermined audio frequency band regarding eachof a plurality of partitioned regions constituting each of the imageswhich are part of the moving image contents and which are extractedtherefrom with the second frequency higher than the first frequency, thesecond layer summary data having the second size.

Preferably, the first and the second frequencies may be those with whichthe images are extracted from the moving image contents eitherperiodically or nonperiodically.

Preferably, the periodical image extraction may mean extracting theimages at intervals of a predetermined number of frames; and thenonperiodical image extraction may mean extracting the images every timea scene change occurs in the moving image content or every time a silentpart of audio data is followed by a nonsilent part thereof.

Preferably, the image processing apparatus of the present embodiment mayfurther include a compression unit configured to connect differentregions between the moving image content retrieved by the second searchsection on the one hand and the moving image content from which thefirst layer summary data is generated by the first layer summary datageneration section on the other hand, so as to delete either of thecontents for moving image content data compression.

According to another embodiment of the present invention, there isprovided an image processing method for use with an image processingapparatus including a first layer summary data generation sectionconfigured such that out of images extracted with a first frequency fromthe images making up a moving image content, the first layer summarydata generation section generates first layer summary data of a firstsize before registering the first layer summary data to a database; asecond layer summary data generation section configured such that out ofimages extracted with a second frequency higher than the first frequencyfrom the images making up the moving image content, the second layersummary data generation section generates second layer summary data of asecond size smaller than the first size; a first search sectionconfigured such that based on the first layer summary data generated bythe first layer summary data generation section, the first searchsection searches the database for a corresponding moving image content;and a second search section configured such that based on the secondlayer summary data generated by the second layer summary data generationsection, the second search section searches the moving image contentsretrieved by the first search section for a corresponding moving imagecontent. The image processing method including the steps of: causing thefirst layer summary data generation section to generate, out of theimages extracted with the first frequency from the images making up themoving image content, the first layer summary data of the first sizebefore registering the first layer summary data to the database; causingthe second layer summary data generation section to generate, out of theimages extracted with the second frequency higher than the firstfrequency from the images making up the moving image content, the secondlayer summary data of the second size smaller than the first size;causing the first search section to search the database for thecorresponding moving image content based on the first layer summary datagenerated by the first layer summary data generation step; and causingthe second search section to search the moving image contents retrievedby the first search step for the corresponding moving image contentbased on the second layer summary data generated by the second layersummary data generation step.

According to a further embodiment of the present invention, there isprovided a program for use with a computer controlling an imageprocessing apparatus including a first layer summary data generationsection configured such that out of images extracted with a firstfrequency from the images making up a moving image content, the firstlayer summary data generation section generates first layer summary dataof a first size before registering the first layer summary data to adatabase; a second layer summary data generation section configured suchthat out of images extracted with a second frequency higher than thefirst frequency from the images making up the moving image content, thesecond layer summary data generation section generates second layersummary data of a second size smaller than the first size; a firstsearch section configured such that based on the first layer summarydata generated by the first layer summary data generation section, thefirst search section searches the database for a corresponding movingimage content; and a second search section configured such that based onthe second layer summary data generated by the second layer summary datageneration section, the second search section searches the moving imagecontents retrieved by the first search section for a correspondingmoving image content. The program causing the computer to execute aprocedure including the steps of: causing the first layer summary datageneration section to generate, out of the images extracted with thefirst frequency from the images making up the moving image content, thefirst layer summary data of the first size before registering the firstlayer summary data to the database; causing the second layer summarydata generation section to generate, out of the images extracted withthe second frequency higher than the first frequency from the imagesmaking up the moving image content, the second layer summary data of thesecond size smaller than the first size; causing the first searchsection to search the database for the corresponding moving imagecontent based on the first layer summary data generated by the firstlayer summary data generation step; and causing the second searchsection to search the moving image contents retrieved by the firstsearch step for the corresponding moving image content based on thesecond layer summary data generated by the second layer summary datageneration step.

According to the present invention embodied as outlined above, out ofimages extracted with a first frequency from the images making up amoving image content, first layer summary data of a first size isgenerated before being registered to a database. Out of images extractedwith a second frequency higher than the first frequency from the imagesmaking up the moving image content, second layer summary data of asecond size smaller than the first size is generated. The database issearched for a corresponding moving image content based on the firstlayer summary data. And the retrieved moving image contents are searchedfor a corresponding moving image content based on the second layersummary data.

The image processing apparatus of the present embodiments may be anindependent apparatus or a block that performs image processing.

According to embodiments of the present invention, it is possible easilyto search and manage a large number of moving image contents.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a typical structure of an imageprocessing apparatus as an embodiment of the present invention;

FIG. 2 is a flowchart explanatory of an image storage process;

FIG. 3 is a flowchart explanatory of a first layer summary datageneration process;

FIG. 4 is a schematic view explanatory of the first layer summary datageneration process;

FIG. 5 is a flowchart explanatory of a clustering process;

FIG. 6 is a schematic view explanatory of a second layer summary datageneration process;

FIG. 7 is a flowchart explanatory of an initial clustering process;

FIG. 8 is a schematic view explanatory of the initial clusteringprocess;

FIG. 9 is another schematic view explanatory of the initial clusteringprocess;

FIG. 10 is a flowchart explanatory of a search and extraction process;

FIG. 11 is a schematic view explanatory of the search and extractionprocess;

FIG. 12 is another schematic view explanatory of the search andextraction process;

FIG. 13 is a flowchart explanatory of a compression process;

FIG. 14 is a schematic view explanatory of the compression process; and

FIG. 15 is a schematic view explanatory of a typical structure of ageneral-purpose personal computer.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[Typical Structure of the Image Processing Apparatus]

FIG. 1 shows a typical structure of an image processing apparatus 11 asan embodiment of the present invention. The image processing apparatus11 in FIG. 1 stores moving image contents and allows the stored contentsto be searched for and extracted as needed.

The image processing apparatus 11 includes an image acquisition unit 21,a buffer 22, a moving image content data registration unit 23, a storageunit 24, a search and extraction unit 25, a content file compressionunit 26, and a display unit 27.

The image acquisition unit 21 acquires moving image contents to beregistered or sample data as part of the moving image contents targetedfor search, and temporarily stores what is acquired into the buffer 22.The sample data may typically include portions of moving image contentsthat can be downloaded over the Internet, images related to thecontents, and sample moving images.

From the moving image content stored in the buffer 22 preparatory toregistration, the moving image content data registration unit 23generates first layer summary data and second layer summary data. Themoving image content data registration unit 23 registers the first layersummary data to a first layer summary data database (DB) 101 of thestorage unit 24. Also, the moving image content data registration unit23 stores a second layer summary data file 112 made of the second layersummary data paired with a moving image content file 111 composed of themoving image content, into the storage unit 24 as a content data pair102. In this case, if the moving image contents held in the storage unit24 are clustered (i.e., grouped into classes), the moving image contentdata registration unit 23 classifies a given moving image content intoone of the classes based on the first layer summary data, before storingthe moving image content in question.

The storage unit 24 in FIG. 1 includes the first layer summary datadatabase 101 and a plurality of classes A through D. The classes Athrough D include content data pairs 102-1 through 102-a, 102-b through102-c, 102-d through 102-e, and 102-f through 102-g, respectively. Thecontent data pairs 102-1 through 102-a include moving image contentfiles 111-1 through 111-a and second layer summary data files 112-1through 112-a, respectively. The content data pairs 102-b through 102-cinclude moving image content files 111-b through 111-c and second layersummary data files 112-b through 112-c, respectively. The content datapairs 102-d through 102-e include moving image content files 111-dthrough 111-e and second layer summary data files 112-d through 112-e,respectively. The content data pairs 102-f through 102-g include movingimage content files 111-f through 111-g and second layer summary datafiles 112-f through 112-g, respectively.

Where there is no particular need to distinguish between these files,the content data pairs may simply be referred to as the content datapair 102, the moving image content files as the moving image contentfile 111, and the second layer summary data files as the second layersummary data file 112. The same also applies to the other components andrelated elements of the image processing apparatus 11. The classes Athrough D are structured in such a manner that information is simplyrecorded to each class corresponding to the first layer summary datawhich is managed by the first layer summary data database 101 and whichbelongs to the class in question. There are no individual folders or thelike named classes A through D in the storage unit 24 for managementpurposes. The storage unit 24 is shown in FIG. 1 in a manner merelyrevealing its class structure schematically. In practice, the contentdata pairs 102 are not recorded individually to regions arrangeduniformly by class. Also, although the moving image content file 111 andthe second layer summary data file 112 making up the content data pair102 are recorded as related to each other, these files are separatelyprovided files that can be managed individually.

The moving image content data registration unit 23 includes an activitycalculation section 41, an image slide section 42, a first layer summarydata generation section 43, a second layer summary data generationsection 44, and a clustering section 45. The activity calculationsection 41 obtains as an activity image the image with its pixel valuerepresented by the difference in pixel value between adjacent pixels ofan image included in a given moving image content. The image slidesection 42 slides the position of the highest activity to the center ofthe image based on the activity image calculated by the activitycalculation section 41. That is, since the position of the highestactivity in the image visually attracts the human's attention the most,that position is moved into the center of the image so as to normalizethe first and the second layer summary data to be extracted.

The first layer summary data generation section 43 includes an imagedivision block 61, an RGB pixel value average calculation block 62, anda scene change detection block 63. The first layer summary datageneration section 43 generates first layer summary data from a frame ofinterest which occurs at predetermined intervals in the input movingimage content or which occurs at the time of a scene change detected bythe scene change detection block 63. That is, the first layer summarydata generation section 43 controls the image division block 61 todivide the frame of interest into a predetermined number of partitionedregions. The first layer summary data generation section 43 alsocontrols the RGB pixel value average calculation block 62 to calculateaverages of the pixel values regarding each of the RGB pixels (red,green and blue pixels) in each of the partitioned regions. The firstlayer summary data generation section 43 then obtains as first layersummary data a frame-based vector which is formed by the elementscomposed of the calculated pixel value averages regarding each of theRGB pixels in each of the partitioned regions and which is about 100bytes in amount, and registers the acquired first layer summary data tothe first layer summary data database 101.

The second layer summary data generation section 44 includes an imagedivision block 71 and an RGB pixel value average calculation block 72,and generates second layer summary data from all frames constituting agiven moving image content. The image division block 71 and RGB pixelvalue average calculation block 72 are the same as the image divisionblock 61 and RGB pixel value average calculation block 62 of the firstlayer summary data generation section 43, respectively.

That is, the second layer summary data generation section 44 controlsthe image division block 71 to divide the frame of interest into apredetermined number of partitioned regions. The second layer summarydata generation section 44 also controls the RGB pixel value averagecalculation block 72 to calculate averages of the pixel values regardingeach of the RGB pixels (red, green and blue pixels) in each of thepartitioned regions. The second layer summary data generation section 44then obtains as second layer summary data a frame-based vector which isformed by the elements composed of the calculated averages of the RGBpixels in each of the partitioned regions and which is about 30 bytes inamount. Furthermore, the second layer summary data generation section 44stores into the storage unit 24 a content data pair 102 constituted by asecond layer summary data file 112 in which the second layer summarydata thus obtained are arrayed chronologically and by the moving imagecontent file 111.

The first layer summary data and the second layer summary data eachinclude information indicating which moving image content file the datain question belongs to. Although the first layer summary data is set tobe about 100 bytes in amount obtained at intervals of 30 frames and thesecond layer summary data is set to be about 30 bytes in amount obtainedper frame, the first layer summary data may be acquired at intervals ofa different number of frames and may be formed by a different amount ofinformation as long as the first layer summary data has a lowerfrequency of frames and is formed by a larger quantity than the secondlayer summary data. That is, the first layer summary data may be data ofa relatively large quantity because it is individually searched for andretrieved from the first layer summary data database 101. On the otherhand, the second layer summary data individually needs to have a smalldata size because it is used in units of the second layer summary datafile 112 containing a plurality of second layer summary data that aremanaged chronologically.

The clustering section 45 includes a gravity center calculation block81, a distance calculation block 82, and an initial registration block83. Based on the first layer summary data registered in the first layersummary data database 101, the clustering section 45 classifies (i.e.,clusters) those of a plurality of content data pairs 102 which aresimilar to one another and which are stored in the storage unit 24 intothe same class.

When a new moving image content file 111 is to be registered to thestorage unit 24 whose clustering has been complete, the clusteringsection 45 classifies the new file based on the first layer summary dataof the moving image content to be registered anew. That is, theclustering section 45 first controls the gravity center calculationblock 81 to calculate the gravity center vectors of the first layersummary data made of the vectors belonging to various classes. Theclustering section 45 then controls the distance calculation block 82 tocalculate the distance between the gravity center of each of the classesinvolved on the one hand, and the vector constituting the first layersummary data of the moving image content to be registered anew on theother hand. The clustering section 45 classifies the moving imagecontent to be newly registered into the class having the gravity centerat the shortest distance to the vector made of the first layer summarydata of the moving image content to be added anew.

If the content data pairs 102 registered in the storage unit 24 have yetto be classified, the clustering section 45 clusters these content datapairs 102 into classes. That is, the clustering section 45 controls thedistance calculation block 82 to calculate the distances between thevectors making up the first layer summary data which represent themoving image contents and which are registered in the first layersummary data database 101, in order to cluster the moving image contentdata found within a predetermined range of distances into the sameclass. The clustering section 45 also controls the initial registrationblock 83 to register the classes of the first layer summary data intowhich the moving image contents have been clustered.

The search and extraction unit 25 includes an activity calculationsection 151, an image slide section 152, a first layer summary datasearch section 153, a second layer summary data search and extractionsection 154, a class search section 155, and a search result displayimage generation section 156. The search and extraction unit 25 searchesthe moving image contents based on the sample data related to the movingimage content which was acquired by the image acquisition unit 21 andplaced into the buffer 22 and which is targeted for search. The searchand extraction unit 25 displays the result of the search on the displayunit 27.

More specifically, the activity calculation section 151 and image slidesection 152 are the same as the activity calculation section 41 andimage slide section 42 of the moving image content data registrationunit 23, respectively. That is, the activity calculation section 151calculates the activities formed by the difference values between theadjacent pixels in images (i.e., frames) related to the moving imagecontent targeted for search, so as to generate an activity imagecomposed of the calculated activities as its pixel values. The imageslide section 152 slides the position of the largest activity into thecenter of the activity image thus calculated.

The first layer summary data search section 153 includes an imagedivision block 171, an RGB pixel value average calculation block 172 anda comparison block 173. The first layer summary data search section 153generates first layer summary data from a representative image of thesample data related to the moving image content targeted for search.That is, the first layer summary data search section 153 first controlsthe image division block 171 to divide the representative image into aplurality of partitioned regions. The first layer summary data searchsection 153 then controls the RGB pixel value average calculation block172 to obtain averages of the pixels regarding each of the RGB pixels(red, green and blue pixels) in each of the partitioned regions. Thefirst layer summary data search section 153 then obtains as first layersummary data a frame-based vector which is formed by the elementscomposed of the calculated pixel value averages regarding each of theRGB pixels in each of the partitioned regions and which is about 100bytes in amount. Incidentally, the representative image of given sampledata is either the first image of the moving image content making up thesample data, or the image occurring at the time of each scene change.

The first layer summary data search section 153 controls the comparisonblock 173 to make comparisons between the vectors constituting firstlayer summary data of the representative image obtained from the sampledata on the one hand, and the vectors constituting the first layersummary data registered in the first layer summary data database 101 onthe other hand, so as to determine whether there is a match or asimilarity therebetween. Also, the first layer summary data searchsection 153 searches candidates of the moving image content to besearched for corresponding to the first layer summary data of the sampledata. In this case, it is assumed that the class search section 155 hasobtained beforehand the gravity center vector of a matched classfollowing the comparisons to determine whether there is a match or asimilarity between the vector of the first layer summary data of therepresentative image on the one hand, and the gravity center vectors ofthe first layer summary data clustered into various classes on the otherhand. On that assumption, the first layer summary data search section153 searches for the moving image content corresponding to the firstlayer summary data of the sample data based on the result of thecomparisons indicating either a match with or a similarity to the firstlayer summary data belonging to the target class. That is, each class ismade up of the first layer summary data of which the distances betweenthe vectors constituting the data are short. For this reason, from amongthe first layer summary data registered in the first layer summary datadatabase 101, the first layer summary data search section 153 need onlyput to comparison the first layer summary data belonging to the classthat has the gravity center vector matching the first layer summary dataof the representative image. This feature enhances the speed of search.

The second layer summary data search and extraction section 154generates second search summary data based on the moving imageinformation regarding the moving image content of the sample datatargeted for search. The second layer summary data search and extractionsection 154 proceeds to search for a match between the second searchsummary data of the sample data on the one hand, and the second layersummary data file 112 of the moving image contents of the first layersummary data retrieved by the first layer summary data search section153 on the other hand. That is, the second layer summary data search andextraction section 154 controls the image division block 181 to divideinto a predetermined number of partitioned regions each of the framesconstituting the moving image information about the moving image contentof the sample data targeted for search. The second layer summary datasearch and extraction section 154 then controls an RGB pixel valueaverage calculation block 182 to calculate averages of the pixel valuesregarding each of the RGB pixels (red, green and blue pixels) in each ofthe partitioned regions. The second layer summary data search andextraction section 154 then obtains as second layer summary data of thesample data a frame-based vector which is formed by the elementscomposed of the calculated pixel value averages regarding each of theRGB pixels in each of the partitioned regions and which is about 30bytes in amount. That is, the second layer summary data file 112 is afile in which the second layer summary data obtained per frame arearrayed chronologically. Thus if the second layer summary data of thesample data registered and arrayed chronologically in the second layersummary data file 112 are slid frame by frame so as to obtainsequentially the difference between the second layer summary data of thecorresponding frames, and if the smallest of the difference values isfound smaller than a predetermined threshold value, then it isdetermined that a match with or a similarity to the moving image contenttargeted for search has occurred. In the event of a match with or asimilarity to the moving image content targeted for search, the slideposition at which the difference value is the smallest is determined asthe reproduction position for the sample data in question within themoving image content targeted for search.

The class search section 155 obtains the distance between the vectormaking up the first layer summary data acquired from the representativeimage of the sample data on the one hand, and the gravity center vectorof each of the classes registered in the first layer summary datadatabase 101 stored in the storage unit 24 on the other hand, in orderto search for a class that is within a predetermined distance. That is,the class search section 155 controls the gravity center calculationblock 191 to obtain the gravity center vector of the vectorsconstituting the first layer summary data belonging to each of theclasses. The class search section 155 then searches the class having thegravity center vector within the predetermined distance to find thedistance between the vector formed by the first layer summary data ofthe representative image and the gravity center vector.

The search result display image generation section 156 causes thedisplay unit 27 such as an LCD (liquid crystal display) to display animage corresponding to the first layer summary data of the moving imagecontent searched for by the second layer summary data search andextraction section 154.

The content file compression unit 26 includes a matched part searchsection 211, a mismatched part search section 212, an edit section 213,and an update section 214. The content file compression unit 26 searchesfor matched and mismatched parts in the second layer summary data file112 retrieved by the second layer summary data search and extractionsection 154, and performs edits such as deleting of the matched partsleaving only one of them and splicing it to the remaining mismatchedparts so as to compress the data amount in the moving image content file111. That is, the content file compression unit 26 controls the matchedpart detection section 211 to search for a range of matched parts in theretrieved second layer summary data file 112. The content filecompression unit 26 also controls the mismatched part search section 212to search for a range of mismatched parts of the retrieved file. Thecontent file compression unit 26 then controls the edit section 213 toperform edits involving the deleting of the matched parts leaving onlyone of them and connecting it to the mismatched parts as needed. Usingthe moving image content file 111 thus generated anew, the content filecompression unit 26 updates the current moving image content file 111.

[Moving Image Content Storage Process]

Explained below in reference to the flowchart of FIG. 2 is the movingimage content storage process performed by the image processingapparatus 11 shown in FIG. 1.

In step S11, the image acquisition unit 21 acquires the moving imagecontent to be stored into the storage unit 24, and stores the acquiredcontent into the buffer 22.

In step S12, the moving image content data registration unit 23 readsthe moving data content from the buffer 22, performs a first layersummary data generation process to generate first layer summary data,and registers the generated summary data to the first layer summary datadatabase 101 of the storage unit 24.

[First Layer Summary Data Generation Process]

The first layer summary data generation process is explained below inreference to the flowchart of FIG. 3.

In step S31, the moving image content data registration unit 23initializes to “1” a counter “f” that counts the frame.

In step S32, the moving image content data registration unit 23 readssequentially the moving image content to determine whether there isanother frame image yet to be processed. If another unprocessed frameimage is not determined to exist in step S32, the first layer summarydata generation process is terminated. If in step S32 anotherunprocessed frame image of the moving image content is determined toexist, then control is transferred to step S33.

In step S33, the first layer summary data generation section 43 controlsthe scene change detection block 63 to determine whether the suppliedframe image represents a scene change upon comparison with theimmediately preceding image. For example, the scene change detectionblock 63 obtains the total sum of the difference values of the pixelvalues between the pixels of the frame image to be processed and thepixels of the immediately preceding image. If the obtained total sum isfound larger than a predetermined threshold value, then a scene changeis determined to have occurred. That is, in the case of continuousmoving images, the change between the current frame image and theimmediately preceding frame image is considered small and thus the totalsum of the difference values of the pixel values is considered to besmaller than the predetermined threshold value. Upon occurrence of ascene change, the frame image is often quite different from theimmediately preceding frame image and thus the total sum of thedifference values of the pixel values between the pixels is consideredto vary significantly. Given these considerations, the scene changedetection block 63 compares the total sum of the difference values ofthe pixel values between the pixels with the predetermined thresholdvalue to determine whether a scene change has occurred.

If in step S33 no scene change is determined to have occurred becausethe total sum of the difference values of the pixel values between thecurrent and the immediately preceding frame images is smaller than thethreshold value, then control is transferred to step S34.

In step S34, the moving image content data registration unit 23determines whether the counter “f” is set to 30. If the counter “f” isnot at 30, control is transferred to step S35 in which the counter “f”is incremented by 1, and step S32 is reached again. That is, unless ascene change takes place, steps S32 through S35 are repeated until 30frames elapse.

If in step S33 a scene change is determined to have occurred because thetotal sum of the difference values of the pixel values between thecurrent and the immediately preceding frame images is larger than thethreshold value, or if in step S34 the counter “f” is at 30, thencontrol is transferred to step S36.

In step S36, the moving image content data registration unit 23 extractsfrom the buffer 22 the image of the frame of interest that is the 30thframe from the immediately preceding frame.

In step S37, the moving image content data registration unit 23 controlsthe activity calculation section 41 to calculate activities in theextracted image of the frame of interest. More specifically, theactivity calculation section 41 calculates the sum of the differencesbetween pixels horizontally and vertically adjacent to each other as theactivity of each pixel. The activity calculation section 41 performs thesame calculations on all pixels to obtain an activity image formed bythe activities of the pixel values regarding every pixel.

In step S38, the moving image content data registration unit 23 controlsthe image slide section 42 to move, within the obtained activity image,the position of the pixel with the highest activity to the center of theimage constituting the frame of interest. That is, since the position ofhigh activity in an image visually attracts the human's attention themost, that position is slid into the center of the image so that thatposition will draw the viewer's attention most easily. This arrangementnormalizes subsequent processing. If there exist a plurality of pixelshaving the highest activity each, then the viewer's attention is drawnto an object formed by these highest-activity pixels. In this case, thecenter of gravity of these pixels may be moved into the center of theimage making up the frame of interest.

In step S39, the first layer summary data generation section 43 controlsthe image division block 61 to divide into N partitioned regions P(n)the F-th frame of which the center position is slid. For example, if thenumber N is 32, then the F-th frame is divided into partitioned regionsP(1) through P(32) as shown in FIG. 4.

In step S40, the first layer summary data generation section 43initializes to 1 a counter “n” that counts the partitioned region P(n).

In step S41, the first layer summary data generation section 43 controlsthe RGB pixel value average calculation block 62 to calculate averagesof the pixel values regarding each of the RGB pixels in each of thepartitioned regions.

In step S42, the first layer summary data generation section 43determines whether the counter “n” is at a predetermined number N ofpartitions. In the example of FIG. 4, the first layer summary datageneration section 43 determines whether the counter “n” is at thepredetermined partition count N=32. If in step S42 the counter “n” isnot determined to have the predetermined partition count N, then stepS43 is reached. In step S43, the first layer summary data generationsection 43 increments the counter “n” by 1, and control is returned tostep S41. That is, the averages of the pixel values regarding each ofthe RGB pixels in all partitioned regions P(n) are obtained.

If in step S42 the counter “n” is determined to have reached thepartition count N, then control is transferred to step S43.

In step S44, the first layer summary data generation section 43 thenobtains first layer summary data C1 which is formed by the vector withits elements composed of the calculated pixel value averages regardingeach of the RGB pixels in each of the partitioned regions P(1) throughP(N) and which is about 100 bytes in amount, and registers the acquiredfirst layer summary data C1 to the first layer summary data database 101for storage into the storage unit 24. Control is then returned to stepS31.

When the above steps have been carried out, the frames of interest areobtained sequentially from the target moving image content at intervalsof 30 frames or upon each scene change. Each of the frames of interestis divided into a predetermined number of partitioned regions P(n), andthe pixel value averages regarding each of the RGB pixels in eachpartitioned region are obtained. Then the first layer summary data C1which is formed by the vector with its elements composed of thecalculated pixel value averages regarding each of the RGB pixels in eachof the partitioned regions P(n) and which is about 100 bytes in amountis obtained and registered sequentially to the first layer summary datadatabase 101. For example, if the frame of interest is divided into 32partitioned regions as shown in FIG. 4 and if each of the RGB pixelsindicative of one of 16 gradations is expressed by one byte, then thefirst layer summary data C1 is formed by a vector of 96 dimensionscomposed of 96 bytes each representing one element of the vector. Forthis reason, the information registered in the first layer summary datadatabase 101 may be considered to be feature quantities made up of setsof the vectors constituting the first layer summary data excludingchronological information.

The explanation is now returned to the flowchart of FIG. 2.

In step S12, the first layer summary data of the moving image data to beregistered is registered to the first layer summary data database 101 ofthe storage unit 24. In step S13, the clustering section 45 performs aclustering process thereby classifying the first layer summary data ofthe moving image content to be registered.

[Clustering Process]

The clustering process is explained below in reference to the flowchartof FIG. 5.

In step S51, from the first layer summary data registered in the firstlayer summary data database 101, the clustering section 45 extractsthose data which are clustered into classes and which exclude the movingimage content to be registered. Also, the clustering section 45 controlsthe gravity center calculation block 81 to calculate the gravity centervector of the vectors made up of the first layer summary data clusteredin the same class. For example, with regard to the class A shown in FIG.1, the clustering section 45 controls the gravity center calculationblock 81 to calculate the gravity center vector of the vectors composedof the first layer summary data corresponding to each of the contentdata pairs 102-1 through 102-a of the class A. In like manner, theclustering section 45 causes the gravity center calculation block 81 tocalculate the gravity center vector of the vectors made up of the firstlayer summary darn corresponding to each of the classes B through D.

In step S52, out of the first layer summary data of the moving imagedata to be registered, the clustering section 45 sets unprocessedsummary data as the first layer summary data C1 to be processed.

In step S53, the clustering section 45 initializes to 1 a counter “m”that counts the class.

In step S54, the clustering section 45 controls the distance calculationblock 82 to calculate the distance dm between the vector constitutingthe first layer summary data C1 to be processed and the gravity centervector of the class “m.” More specifically, the distance calculationblock 82 may use square norm in calculating a distance dm between thevectors.

In step S55, the clustering section 45 determines whether thevector-to-vector distance dm thus obtained is smaller than apredetermined threshold value th1. If the vector-to-vector-distance dmis determined to be smaller than the threshold value th1, control istransferred to step S56.

In step S56, the clustering section 45 temporarily registers the firstlayer summary data C1 targeted to be processed to the class “m.”

In step S57, the clustering section 45 determines whether the counter“m” has reached the number M of clustered classes. If it is determinedthat the counter “m” has yet to reach the number M, control istransferred to step S58. In step S58, the clustering section 45increments the counter “m” by 1, and control is returned to step S54.That is, steps S54 through S58 are repeated until the distance betweenthe gravity center vector of every class and the vector of the firstlayer summary data C1 is determined to be smaller than the predeterminedthreshold value th1.

If in step S57 the counter “m” is determined to have reached the classcount M, step S59 is reached. In step S59, the clustering section 45determines whether the first layer summary data C1 targeted to beprocessed is clustered into any of the classes and temporarilyregistered thereto. If the first layer summary data C1 is determined tohave been clustered into any of the classes and temporarily registeredthereto, then control is transferred to step S60.

In step S60, from among the classes to which the first layer summarydata C1 to be processed is temporarily registered, the clusteringsection 45 clusters the first layer summary data C1 into the class “m”that is at the shortest distance dm. That is, where the first layersummary data C1 to be processed has temporarily been registered to aplurality of classes, the summary data needs to be definitivelyclustered to one of the classes. Thus the first layer summary data C1 tobe processed is clustered into the class whose gravity center vector isthe closest to the vector of the first layer summary data C1 in question(i.e., clustered into the most similar class).

In step S61, the clustering section 45 determines whether the firstlayer summary data of the same moving image content has already beenregistered to the selected class. If in step S61 no first layer summarydata of the same moving image content is determined to be registered tothe selected class, then step S62 is reached. In step S62, theclustering section 45 clusters the first layer summary data C1 targetedto be processed into the selected class and thereby registers thesummary data C1 to the first layer summary data database 101.

If in step S59 the first layer summary data C1 in question has yet to beclustered into any of the classes, or if in step S61 the first layersummary data of the same moving image content has already beenregistered to the selected class, then control is transferred to stepS63.

In step S63, the clustering section 45 determines whether there is anyunprocessed first layer summary data left in the moving image content tobe registered. If such unprocessed first layer summary data isdetermined to exist, then control is returned to step S52. That is,steps S52 through S63 are repeated until the unprocessed first layersummary data have been exhausted in the moving image content of thesample data.

If in step S63 no further unprocessed first layer summary data isdetermined to exist in the moving image content to be registered, thenthe clustering process is brought to an end.

When the above steps have been carried out, a maximum of one first layersummary data item is clustered into each class with regard to the firstlayer summary data of the moving image content to be registered.

The explanation is now returned to the flowchart of FIG. 2.

In step S13, a maximum of one first layer summary data item is clusteredinto each class regarding the first layer summary data of the movingimage content targeted to be registered. Control is then transferred tostep S14.

In step S14, the second layer summary data generation section 44performs a second layer summary data generation process to generate asecond layer summary data file 112 made of the second layer summarydata. The second layer summary data generation section 44 stores thegenerated second layer summary data file 112 into the storage unit 24together with a moving image content file 111 in the form of a contentdata pair 102.

[Second Layer Summary Data Generation Process]

The second layer summary data generation process is explained below inreference to the flowchart of FIG. 6.

In step S81, the moving image content data registration unit 23initializes to 1 a counter F that counts the number of frames.

In step S82, the moving image content data registration unit 23determines whether there is another frame image which is held in thebuffer 22 and which is part of the moving image content to beregistered. If there is no further frame image left in the moving imagecontent, the second layer summary data generation process is terminated.If another frame image is determined to exist in step S82, control istransferred to step S83.

In step S83, the moving image content data registration unit 23 extractsfrom the buffer 22 the frame (F) as the image of the frame of interest.

In step S84, the moving image content data registration unit 23 controlsthe activity calculation section 41 to calculate an activity imageregarding the image of the extracted frame of interest.

In step S85, the moving image content data registration unit 23 controlsthe image slide section 41 to slide, within the obtained activity image,the position of the pixel with the highest activity to the center of theimage of the frame of interest.

In step S86, the second layer summary data generation section 44controls the image division block 71 to divide the F-th frame of whichthe center position is slid, into N partitioned regions P(n).

In step S87, the second layer summary data generation section 44initializes to 1 the counter “n” that counts the partitioned regionP(n).

In step S88, the second layer summary data generation section 44controls the RGB pixel value average calculation block 72 to obtainaverages of the pixel values regarding each of the RGB pixels in thepartitioned region P(n).

In step S89, the second layer summary data generation section 44determines whether the counter “n” is at a predetermined partition countN. If in step S89 the counter “n” is not determined to have reached thepredetermined partition count N, then step S90 is reached. In step S90,the second layer summary data generation section 44 increments thecounter “n” by 1, and control is returned to step S88. That is, theaverages of the pixel values regarding each of the RGB pixels in allpartitioned regions P(n) are acquired.

If in step S90 the counter “n” is determined to have reached thepartition count N, then control is transferred to step S91.

In step S91, the second layer summary data generation section 44registers to a new second layer summary data file 112 second layersummary data C2 which is formed by a vector with its elements composedof the calculated pixel value averages regarding each of the RGB pixelsin each of the partitioned regions P(1) through P(N) and which is about30 bytes in amount. In this case, the second layer summary datageneration section 44 registers the second layer summary data C2 to thenew second layer summary data file 112 in the order of processed framesfor storage into the storage unit 24.

In step S92, the counter F is incremented by 1. Control is then returnedto step S82.

When the above steps have been carried out, each of the frames making upthe moving image content is taken sequentially as the frame of interestand divided into the predetermined number of partitioned regions P(n).The averages of the pixel values regarding each of the RGB in each frameof interest are obtained. The second layer summary data C2 which is avector with its elements composed of the pixel value averages regardingeach the RGB pixels in each of the partitioned regions P(n) and which isabout 30 bytes in amount is registered sequentially to the second layersummary data file 112 in the order of processed frames. For example, ifthe frame of interest is divided into 32 partitioned regions as shown inFIG. 4 and if each of the RGB pixels indicative of one of fourgradations is expressed by two bits, then one second layer summary dataitem C2 is formed by a vector of 96 dimensions composed of 24 bytes.Also, the second layer summary data file 112 is structured in such amanner that the second layer summary data C2 is stored thereinchronologically in order of frames. For this reason, the data made up ofthe second layer summary data C2 stored in the order of frames in thesecond layer summary data file 112 may be said to constitute featurequantities composed of the vectors of the second layer summary data C2containing chronological information.

The explanation is now returned to the flowchart of FIG. 2.

In step S14, the second layer summary data file 112 is generated in thesecond layer summary data generation process. Control is thentransferred to step S15.

In step S15, the moving image content data registration unit 23registers to the storage unit 24 the content data pair 102 constitutedby the moving image content file 111 made of the moving image content tobe registered and by the corresponding second layer summary data file112.

Where the moving image content is registered by carrying out the abovesteps, the first layer summary data extracted either at intervals of apredetermined number of frames or in units of a frame occurring uponscene change is registered to the first layer summary data database 101.Also, the second layer summary data extracted in units of a frame isregistered in the order of frames as the second layer summary data file112. The second layer summary data file 112 is paired with the movingimage content file 111 to make up the content data pair 102 that isstored into the storage unit 24.

The information in the first layer summary data database 101 isregistered therein solely as the first layer summary data C1constituting a database extracted not in units of a frame but atintervals of a predetermined number of frames or in units of a frameoccurring upon scene change. Thus the information in the first layersummary data database 101 has no chronological information and isregistered to serve as a database conducive to enhanced data search. Forthis reason, the information in the first layer summary data database101 is considered to offer feature quantities highly suitable fordetermining the moving image content of the sample data targeted forsearch through comparisons with the data of the moving image content ofthe target sample data for search.

Because the first layer summary data is clustered when registered to thefirst layer summary data database 101, it is possible, upon search forgiven first layer summary data, to determine which class the first layersummary data of interest belongs to, before searching the determinedclass for the first layer summary data in question. This arrangement canboost the speed of search.

[Initial Clustering Process]

For the preceding steps, it was assumed that all moving image contentfiles 111 registered in the storage unit 24 have already been clusteredinto classes. However, if the moving image content files 111 areregistered in the storage unit 24 without being clustered, an initialclustering process is needed. The initial clustering process isexplained below in reference to the flowchart of FIG. 7. It is assumedhere that the first layer summary data about all moving image contentshave already been registered to the first layer summary data database101 following the first layer summary data generation process. Thus itis assumed that the moving image contents are stored in the storage unit24 without being clustered in steps S11, S12, S14 and S15 (step S13 isexcluded) in the flowchart of FIG. 2.

In step S111, the clustering section 45 initializes counters “q” and “r”to 1 and 2, respectively. The counter “q” identifies the first layersummary data C1(q) of interest and the counter “r” identifies the firstlayer summary data C1(r) targeted for comparison within the first layersummary data database 101.

In step S112, the clustering section 45 controls the distancecalculation block 82 to calculate the distance dm between the firstlayer summary data C1(q) and the first layer summary data C1(r).

In step S113, the clustering section 45 determines whether thecalculated distance dm is shorter than a predetermined threshold valueth1. If the distance dm is determined to be shorter than the thresholdvalue th1 (i.e., where a similarity is detected), then control istransferred to step S114.

In step S114, the clustering section 45 causes the initial registrationblock 83 to determine whether any other first layer summary data C1 ofthe moving image content to which the first layer summary data C1(r)belongs has already been clustered and registered to the same class asthe first layer summary data C1(q) of interest.

If in step S114 it is determined that any other first layer summary dataC1 of the moving image content to which the first layer summary dataC1(r) belongs has yet to be clustered and registered to the same classas the first layer summary data C1(q) of interest, then control istransferred to step S115.

In step S115, the initial registration block 83 clusters the first layersummary data C1(r) into the same class as the first layer summary dataC1(q) before registering the summary data C1(r).

That is, if the first layer summary data C1(r) clustered into the sameclass as the first layer summary data C1(q) and targeted for comparisonwith other summary data is already registered, then a plurality ofdifferent first layer summary data derived from the same moving imagecontent would be registered to the same class. Since each class issupposed to accommodate similar or matched frame images between movingimage contents, there is no need to register a plurality of first layersummary data of the same moving image content to the same class. Forthis reason, the first layer summary data C1(r) targeted for comparisonis clustered and registered to the same class as the first layer summarydata C1(q) of interest only if no other first layer summary data of thesame moving image content has already been registered.

In step S116, the clustering section 45 determines whether the counter“r” has reached a total count Q of the first layer summary data C1. Ifthe counter “r” is not determined to be at the total count Q, then stepS117 is reached. In step S117, the counter “r” is incremented by 1, andcontrol is returned to step S112.

If in step S113 the distance dm is determined to be longer than thepredetermined threshold value th1, or if in step S114 it is determinedthat some other first layer summary data C1(r) targeted for comparisonand belonging to the same class as the first layer summary data C1(q) ofinterest has already been clustered and registered, then step S115 isskipped. Control is then transferred to step S116.

That is, steps S112 through S116 are repeated until the first layersummary data C1(q) of interest has been compared with all other firstlayer summary data C1(r) targeted for comparison.

If the counter “r” is determined to have reached the total count Q instep S116, step S118 is reached. In step S118, the clustering section 45determines whether the counter “q” has reached the total count Q. If thecounter “q” is not determined to have reached the total count Q in stepS116, then step S119 is reached. In step S119, the clustering section 45increments the counter “q” by 1. Then in step S120, the clusteringsection 45 sets the counter “r” to a value larger than that on thecounter “q.” Control is then returned to step S112.

That is, there is no need to perform duplicate comparisons between thefirst layer summary data in the first layer summary data database 101.The first layer summary data C1(r) targeted for comparison with thefirst layer summary data C1(q) of interest need only be larger than thevalue on the counter “q.” Therefore the counter “r” counts the number oftimes the process is performed starting from q+1 to the total count Q.Steps S112 through S120 are repeated until these comparisons have beencarried out between all first layer summary data.

If in step S118 the counter “q” is determined to have reached the totalcount Q, then the initial clustering process is brought to an end.

When the above steps have been carried out, similar or matched firstlayer summary data are clustered into the same class. Also, a maximum ofone first layer summary data item belonging to the same moving imagecontent is clustered into each class. As a result, if each class isformed by deriving, say, six first layer summary data items fromdifferent moving image contents, then there are extracted six frameimages such as those shown in the top row of FIG. 8 indicating the sameperson. Alternatively, there may be extracted six frame images in thesecond row from the top in FIG. 8 showing the same lighthouse and thesea, six frame images in the third row from the top showing twoaircraft, or six frame images in the fourth row from the top showing thesame rocket launching pad. The six frame images in each of the rows inFIG. 8 are clustered into the same class. That is, the first layersummary data made up of the six frame images in each row of FIG. 8constitute the images of which the vector-to-vector distances fallwithin a predetermined range.

When the number of classes is obtained for each of the numbers of thefirst layer summary data clustered into the same class, a tendency suchas one shown in FIG. 9 is known to appear. That is, in FIG. 9, thevertical axis denotes the number of the first layer summary dataclustered into the same class (i.e., number of the frame imagesclustered into the same class), and the horizontal axis represents thenumber of classes. Consequently, it can be seen that there are numerousclasses each having a small number (e.g., 3 to 5) of first layer summarydata items clustered thereinto and that there exist an extremely smallnumber of classes each having a large number (e.g., 16 or more) of firstlayer summary data items clustered thereinto. From this, it isunderstood that identifying one of the classes having the numerous firstlayer summary data items therein can also identify the correspondingmoving image content. Thus upon search for a given moving image content,it can be seen that the load involved in the search process is reducedand the speed of search is improved by identifying the class and bymaking comparisons with the first layer summary data belonging to theidentified class. It is also possible to perform data mining using theclass distribution unmodified such as is shown in FIG. 9. For example,where the same TV program is stored every day as moving image contents,it is possible statistically to analyze the scenes that have beenutilized with a high frequency in a given month.

[Search and Extraction Process]

A search and extraction process is explained below in reference to theflowchart of FIG. 10.

In step S141, the image acquisition unit 21 acquires the moving imagecontent making up the sample data of the moving image content targetedfor search, and stores the acquired content into the buffer 22.

In step S142, the search and extraction unit 25 extracts the image ofthe frame constituting a representative image from the moving image ofthe sample data held in the buffer 22. Also, the search and extractionunit 25 controls the activity calculation section 151 to calculate anactivity image of the frame making up the representative image of themoving image content targeted for search.

In step S143, the search and extraction unit 25 controls the image slidesection 152 to move, within the acquired activity image, the position ofthe pixel with the highest activity into the center of the image of theframe constituting the representative image.

In step S144, the first layer summary data search section 153 controlsthe image division block 171 to divide into N partitioned regions P(n)the frame making up the representative image of which the centerposition is slid.

In step S145, the first layer summary data search section 153initializes to 1 the counter “n” that counts the partitioned regionP(n).

In step S146, the first layer summary data search section 153 controlsthe RGB pixel value average calculation block 172 to obtain averages ofthe pixel values regarding each of the RGB pixels in the partitionedregion P(n).

In step S147, the first layer summary data search section 153 determineswhether the counter “n” is at a predetermined partition count N. If instep S147 the counter “n” is not determined to have reached thepredetermined partition count N, step S148 is reached. In step S148, thefirst layer summary data search section 153 increments the counter “n”by 1, and control is returned to step S146. That is, the averages of thepixel values regarding each of the RGB pixels in all partitioned regionsP(n) are obtained.

If in step S147 the counter “n” is determined to have reached thepartition count N, control is transferred to step S149.

In step S149, the first layer summary data search section 153 stores thefirst layer summary data C1 t which is formed by a vector with itselements composed of the calculated pixel value averages regarding eachof the RGB pixels in each of the partitioned regions P(1) through P(N),which is about 100 bytes in amount, and which makes up therepresentative image of the sample data with regard to the moving imagecontent targeted for search. Control is then transferred to step S150.

In step S150, the class search section 155 extracts the first layersummary data clustered into classes from among those first layer summarydata registered in the first layer summary data database 101. Also, theclass search section 155 controls the gravity center calculation block191 to calculate the gravity center vector of the vectors made up of thefirst layer summary data clustered into the same class.

In step S151, the class search section 155 initializes to 1 the counter“m” that identifies the class.

In step S152, the class search section 155 controls a comparison block192 to compare the vector of the first layer summary data C1 t of theframe of the representative image with the gravity center vector of theclass “m” so as to determine a match (or a similarity) therebetween. Inthis case, the vectors of, say, (3, 4, 0, 4) and (2, 4, 1, 4) expressedin three bits using values ranging from 0 to 7 are mapped to vectors (1,2, 0, 2) and (1, 2, 0, 2) expressed in two bits using values 0 through3. If such bit conversion between vectors results in a match or asimilarity, then the vectors involved may be determined to be matchedwith or similar to one another. In another example, the vectors of, say,(3, 2, 3, 0) and (4, 2, 4, 0) in three bits result in (1, 1, 1, 0) and(2, 1, 2, 0) in two bits after undergoing simple three-to-two-bitconversion, the result being a mismatch. However, if supplemented byhalf the quantization step involved (i.e., 1), the initial three-bitvectors are made into vectors (4, 3, 4, 1) and (5, 3, 5, 1) which inturn result in vectors (2, 1, 2, 0) and (2, 1, 2, 0) followingthree-to-two-bit conversion, the result being a match. That is, if thevectors involved are found to match with each other or similar to oneanother when supplemented by half the quantization step followed bythree-to-two-bit conversion during quantization, then such a match or asimilarity may be recognized as valid. This arrangement makes itpossible to reduce the raised possibility of mismatches stemming from anextremely narrow range of search dictated by the comparison betweenvectors being considered a match only upon perfect numerical matchtherebetween.

In step S152, it is determined whether the vector of the first layersummary data C1 t matches the gravity center vector of the class “m.” Ifthe two vectors are determined to match with each other in step S152,then step S153 is reached. In step S153, the class search section 155registers the class “m.” If it is determined in step S152 that thevector of the first layer summary data C1 t does not match the gravitycenter vector of the class “m,” then step S153 is skipped.

In step S154, the class search section 155 determines whether thecounter “m” has reached the class count M. If the counter “m” is notdetermined to have reached the class count M, then step S155 is reached.In step S155, the counter “m” is incremented by 1, and control istransferred to step S152.

When the comparisons with the gravity center vector of every class “m”have been completed, the counter “m” is determined to have reached theclass count M in step S154. Control is then transferred to step S156.

In step S156, the first layer summary data search section 153 selectsthe set of the class “m” registered in step S153 as a class “x” targetedfor processing. Alternatively, the class “x” may be arranged torepresent a plurality of classes “m.”

In step S157, the first layer summary data search section 153initializes to 1 a counter “s” that identifies the first layer summarydata C1(s) belonging to the class “x.”

In step S158, the first layer summary data search section 153 controlsthe comparison block 173 to compare the vector of the first layersummary data C1 t constituting the frame of the representative image onthe one hand, and the vector of the first layer summary data C1(s)belonging to the class “x” on the other hand, in order to determine amatch or a mismatch therebetween. Alternatively, as explained above inconjunction with the processing of step S152, a match between vectorsmay also be recognized following bit count conversion or subsequent tothe alteration of the number of bits by addition of half thequantization step width involved.

If it is determined in step S158 that the vector of the first layersummary data C1 t matches the vector of the first layer summary dataC1(s) belonging to the class “x,” then step S159 is reached. In stepS159, the first layer summary data search section 153 registers thevector of the first layer summary data C1(s) belonging to the class “x.”If it is determined in step S158 that the vector of the first layersummary data C1 t does not match the vector of the first layer summarydata C1(s) belonging to the class “x,” then step S159 is skipped.

In step S160, the first layer summary data search section 153 determineswhether the counter “s” has reached the total count S of the first layersummary data C1(s) belonging to the class “x.” If the counter “s” is notdetermined to have reached the total count S, then step S161 is reachedand the counter “s” is incremented by 1. Control is then returned tostep S158.

Upon completion of the comparisons with the vectors of the first layersummary data C1(s) belonging to all classes “x,” step S160 is reached.In step S160, the counter “s” is considered to have reached the totalcount S. Control is then transferred to step S162.

In step S162 is retrieved the content data pair 102 corresponding to themoving image content of the first layer summary data C1 that matches thevector of the first layer summary data C1 t extracted from the sampledata of the moving image content targeted for search, from among thevectors of the first layer summary data C1(s) belonging to the class“x.”

In step S163, the second layer summary data search and extractionsection 154 controls the image division block 181 and RGB pixel valueaverage calculation block 182 in the same manner that the second layersummary data generation section 44 controls the image division block 71and RGB pixel value average calculation block 72, thereby performing thesecond layer summary data generation process to generate the secondlayer summary data file 112 of the sample data of the moving imagecontent targeted for search. The second layer summary data generationprocess is the same as the process explained above in reference to theflowchart of FIG. 6 and thus will not be discussed further.

In step S164, the second layer summary data search and extractionsection 154 controls a slide matching search block 183 to slide thesecond layer summary data file of the sample data of the moving imagecontent targeted for search and the second layer summary data file 112included in the content data pair 102 retrieved in step S162, therebyobtaining a degree of similarity in terms of the differential absolutesum between the second layer summary data of the frames.

In step S165, based on the degree of similarity, the second layersummary data search and extraction section 154 determines the movingimage content targeted for search and the reproduction position of thesample data in the moving image content. More specifically, if thesmallest of the degrees of similarity obtained corresponding to thenumber of slid frames is smaller than a predetermined threshold value,then the second layer summary data search and extraction section 154considers that the moving image content of the sample data matches themoving image content of the content data pair 102 retrieved in stepS162, and thereby determines the moving image content of the sample datato be the moving image content targeted for search. In this case, theslide position at which the degree of similarity is the smallest is alsodetermined to be the reproduction position of the sample data in themoving image content targeted for search.

That is, the slide matching search block 183 may typically obtain as thedegree of similarity the differential absolute sum between a secondlayer summary data file 112A of the sample data shown in the top row ofFIG. 11 on the one hand, and a second layer summary data file 112Bretrieved in step S162 and shown in the second row of FIG. 11 on theother hand. In FIG. 11, the second layer summary data file 112A of thesample data is made up of frames F201 through F203. The second layersummary data of these frames are expressed as one-dimensional vectorsdesignated by numerals 10, 11 and 7 in order of the frames. The secondlayer summary data file 112B is composed of frames F111 through F115.The second layer summary data of these frames are also expressed asone-dimensional vectors designated by numerals 8, 10, 11, 7 and 6 inorder of the frames.

In an initial process, the degree of similarity is obtained as thedifferential absolute sum between the second layer summary data file112A in the top row and the second layer summary data file 112B in thesecond row in order of the corresponding frames. In this case, thedegree of similarity is acquired as 7 (=|10−8|+|11−10|+|7−11|). In thenext process, the second layer summary data file 112A is slid right oneframe as shown in the bottom row of FIG. 11. This provides the degree ofsimilarity as 0 (=|10−10|+|11−11|+|7−7|). In this case, it is determinedthat the moving image content of the second layer summary data file 112Bis the moving image content having been searched for from the movingimage from which the second layer summary data file 112A was obtained.Also in this case, it is determined that the timing at which the movingimage content targeted for search is matched is given as the frames F112and F113 in the second layer summary data file 112B. This makes itpossible to determine the reproduction position of the matched movingimage. In the example of FIG. 11, the second layer summary data filesare considered to have matched when the degree of similaritytherebetween turns out to be 0. Alternatively, a match between the filesmay be recognized if the degree of similarity is found to be smallerthan a predetermined threshold value.

For example, as shown in FIG. 12, where the changes in the second layersummary data of the corresponding frames between different moving imagecontents are found to be similar or matched therebetween, these contentsmay be considered the same moving image content.

In FIG. 12, slots 0 through 9 are identifiers identifying moving imagecontents. The horizontal axis in FIG. 12 represents frame numbers andthe vertical axis denotes changes in the second layer summary datacomposed of the one-dimensional vector elements making up each frame. Itshould be noted that the slots 0 through 9 represent waveforms givenafter the slide positions of the frames are adjusted through slidematching to provide for the smallest degree of similarity (i.e., themost similar state).

That is, suppose that in FIG. 12, the waveform indicated by, say, theslot 1 belongs to the second layer summary data file obtained from themoving image content of the sample data. Then that waveform isconsidered to be sufficiently similar to those of the second layersummary data file of the moving image contents corresponding to theslots 2 and 3. Consequently, the moving image content of the slot 1 isfound to match the moving image contents of the slots 2 and 3. It isthen determined that the moving contents targeted for search are thoseindicated by the waveform of the slots 2 and 3.

In step S166, the search result display image generation section 156reads the moving image content file 111 of the same content data pair102 as that of the second layer summary data file 112 whose degree ofsimilarity is lower than a predetermined value. The moving image contentfile 111 thus retrieved is displayed on the display unit 27 togetherwith the reproduction position as the result of search.

Where the above steps have been carried out, it is possible to searcheasily and quickly for a desired moving image content registered in thestorage unit 24 by simply inputting the moving image content of thesample data.

Where hierarchical summary data such as the first and the second layersummary data are in use, a search refinement is first performed based onthe first layer summary data indicative of feature quantities that areon a higher layer and have no chronological information, before a searchfor the target moving image content is carried out using the secondlayer summary data files that are on a lower layer and havechronological information. This makes it possible to reduce the loadinvolved in search and boost the speed of search.

Furthermore, when the first layer summary data is managed in the form ofa database, searches can be performed more easily and at higher speedthan before. Since the first layer summary data is managed in units ofclasses after being clustered thereinto, the first layer summary datacan be refined using the gravity center vector of each class. Only thefirst layer summary data thus refined may be compared with one anotherin detail. This permits implementation of a high-speed search processinvolving the first layer summary data alone.

[Compression Process]

The foregoing description focused on examples in which the target movingimage content is searched for and retrieved easily and quickly by simplyinputting the sample data composed of part of a moving image content.Sometimes, however, large quantities of individually different movingimage contents each containing the same scene may well be retrieved andstored into the storage unit 24 as masses of moving image contentsincluding the overlapping scenes. In such cases, each moving imagecontent file may be compressed by deleting the overlapping scenes and bysplicing only necessary scenes together, whereby the storage capacity ofthe storage unit 24 may be saved significantly.

The above-mentioned compression process is explained below in referenceto FIG. 13.

In step S181, the search and extraction unit 25 performs the search andextraction process to search for matched or similar moving imagecontents based on part of a given moving image content making up thesample data, thereby determining the corresponding contents along withthe reproduction position. The search and extraction process is the sameas the process discussed above in reference to the flowchart of FIG. 10and thus will not be explained further.

In step S182, the content file compression unit 26 controls the matchedpart search section 211 to search a plurality of retrieved moving imagecontent files for matched parts. That is, as explained above inreference to FIG. 11, the second layer summary data files 112 arecompared through slide matching in units of a frame. The comparisonsthrough search determine which of the frames from the moving imagecontent of the sample data match those frames of the moving imagecontents that were considered matched or similar.

If a plurality of moving image contents have been retrieved as a resultof the above comparisons, the matched part of the sample moving imagecontent may be aligned with the retrieved moving image contents in orderto recognize which frames correspond therebetween. Thus the matched partsearch section 211 aligns the matched frames between the moving imagecontent files to search for identical parts. For example, the secondlayer summary data file 112 of a moving image content file Cont1 shownin the top row of FIG. 14 is aligned with the second layer summary datafile 112 of a moving image content file Cont2 shown in the second rowfrom the top, at the position of a frame F1. Of the shaded portions inFIG. 14, those filled with rising diagonal strokes indicating frames Fs1through Fe1 represent the second layer summary data file 112 of themoving image content Cont1, and those with falling diagonal strokesindicating frames Fs2 through F2 e denote the second layer summary datafile 112 of the moving image content Cont2. The matched part searchsection 211 then determines through search that the portion ranging fromthe frame Fs1 to the frame Fe2 in the moving image content Cont1 (orCont2) is identical between the two files.

In step S183, the content file compression unit 26 controls themismatched part search section 212 to search the second layer summarydata files 112 of the retrieved multiple moving image content files formismatched parts. That is, in the case of FIG. 14, it is determined thatthe frames Fs2 through Fs1 of the moving image content Cont2 and theframes Fe2 through Fe1 of the moving image content Cont1 are mismatchedparts.

In step S184, the content file compression unit 26 controls the editsection 213 to splice together the matched and mismatched parts throughediting. That is, the edit section 213 splices together the frames Fs1through Fe2 in the moving image content file 111 of the moving imagecontent Cont1 (or Cont2) corresponding to the second layer summary datafile 112, the frames Fs2 through Fs1 in the moving image content file111 of the moving image content Cont2, and the frames Fe2 through Fe1 inthe moving image content file 111 of the moving image content Cont1. Inthis case, if the moving image content Cont2 is taken as the reference,the frames Fe2 through Fe1 in the moving image content Cont1 need onlybe spliced together through editing as shown in the third row of FIG.14. The splicing generates a grid-like portion constituting part of anew moving image content shown in the third row of FIG. 14. The rowsindicated in FIG. 14 make up the structure of the second layer summarydata file 112. The edit section 213 edits the moving image content file111 based on that structure of the second layer summary data file 112.

In step S185, the update section 214 updates the first layer summarydata database 101 and the content data pair 102 made of the moving imagecontent file 111 and second layer summary data file 112 in a mannerreflecting the newly generated moving image content. In this case, theupdate section 214 deletes the content data pair 102 of the moving imagecontents Cont1 and Cont 2 no longer necessary.

When the above steps have been carried out, the moving image contentfiles can be compressed substantially. This makes it possible to savethe storage capacity of the storage unit 24 accommodating the movingimage contents. Also, the compression process reduces the number of themoving image contents targeted for search, so that the burden of contentmanagement is alleviated. At the same time, it is possible to reduce theload involved in searching for moving image contents based on sampledata and thereby increase the speed of search.

The foregoing description dealt with examples in which the target imageis divided into a plurality of partitioned regions so as to obtain theaverages of the RGB pixel values in each of the partitioned regions foruse as the first and the second layer summary data. However, this is notlimitative of the present embodiment. The feature quantities need onlybe generated in units of a frame. For example, the averages of the RGBpixel values in each partitioned region may be replaced by averages ofbrightness or activity values in each partitioned region. The brightnesshistogram of each partitioned region may also be turned into data. Forexample, the resolution in the direction of brightness may be expressedin three to five bits. If expressed in four bits, the resolution in thedirection of brightness is given as a 16-dimensional vector.Alternatively, the brightness values may be replaced by the RGB pixelvalues. Color solids expressed in RGB pixels may each be divided with aresolution of two to four bits along each of the axes involved, each ofthe divisions being supplemented with a frequency of its appearance. Ifexpressed in three bits, each color solid division may be given as a512-dimensional vector (=83). Furthermore, not only images but alsoaudio information may be used as the basis for generating the first andthe second layer summary data in units of a frame. For example, soundvolumes or the averages of the amplitudes in each of predetermined audiofrequency bands may be used in combination with image information.

Also, color clustering (a common image processing technique forattempting vector quantization on the distribution of the colorsexpressed three-dimensionally using RGB pixels) may be carried out onthe pixels involved. The resulting RGB values may then be expressedusing a three-dimensional vector with the most frequently used colorindicated as the representative color.

Also, there may be cases where the screen is split into smaller regionsso that one of the split regions is dedicated to frequent display oftelop at its top or bottom field. In such cases, the screen regiondevoted to the frequent telop display may be divided with a coarserresolution than the other regions so as to alleviate the effects oftelop.

In the foregoing description, the first layer summary data was shownacquired from the frame of interest which occurs at intervals of 30frames or at the time of a scene change. Alternatively, the first layersummary data may be acquired at other intervals or nonperiodically inkeeping with changes in the moving image content involved. For example,where the periodical solution is desired, the first layer summary datamay be obtained at intervals of a different number of frames. Where thenonperiodical option is preferred, the first layer summary data may beacquired from the frame that occurs at the end of a silent part of audiodata. Also in the foregoing description, the second layer summary datawas shown obtained from every frame. However, the second layer summarydata need only be acquired at intervals of a smaller number of framesthan the first layer summary data. As long as this demand is met, thesecond layer summary data may be obtained periodically (e.g., atintervals of several frames) or nonperiodically whenever a certaincondition (e.g., occurrence of a scene change) is satisfied.

The foregoing description gave examples in which, upon extraction of thefirst and the second layer summary data, the image is normalized byobtaining beforehand an activity image and by taking the position ofhigh activity as the center position. Alternatively, the image may benormalized using a distance over which the auto-correlation function ofthe image is changed by a predetermined amount (e.g., 0.9magnifications). The matched image may then be detected by tolerating ahigher degree of freedom in scaling.

In determining whether a match occurs between vectors upon comparison,the distance therebetween is demanded to fall within a predeterminedvalue. The predetermined value may be varied depending on theapplication. For example, if it is desired to search for a perfectlymatched moving image content, the predetermined value should preferablybe made small. Alternatively, the predetermined value may be allowed tobe large if it is desired to search for matched images by disregarding,say, the presence of telop or of color collection.

In comparing the vectors each made of the first layer summary data inthe above-described search and extraction process, it was determinedwhether a match exists between the vectors. Alternatively, as explainedabove in conjunction with the clustering process and initial clusteringprocess, the distance between the vectors may be obtained so as todetermine whether a sufficient similarity exists therebetween dependingon whether the obtained distance is shorter than a predetermineddistance. Conversely, the determination of whether the vectors match inthe search and extraction process may replace the process of determiningwhether the distance obtained between the vectors reveals a sufficientsimilarity therebetween in the clustering process or initial clusteringprocess.

Also, although the foregoing paragraphs explained examples involving atwo-layer summary data structure formed by the first and the secondlayer summary data, this is not limitative of the present embodiment.Alternatively, a summary data structure of multiple layers may beadopted, with each layer subjected to the clustering process forclassification purposes.

According to the present embodiment, as described above, it is possibleeasily to manage huge quantities of moving image contents as well as tosearch easily for a desired moving image content through such largeamounts of moving image contents being managed.

The series of steps or processes described above may be executed eitherby hardware or by software. Where the software-based processing is to becarried out, the programs constituting the software may be eitherincorporated beforehand in the dedicated hardware of the computer to beused or installed upon use into a general-purpose personal computer orlike equipment capable of executing diverse functions based on theinstalled programs.

FIG. 15 shows a typical structure of a general-purpose personalcomputer. The personal computer incorporates a CPU (central processingunit) 1001. An input/output interface 1005 is connected to the CPU 1001via a bus 1004. A ROM (read only memory) 1002 and a RAM (random accessmemory) 1003 are also connected to the bus 1004.

The input/output interface 1005 is connected with an input section 1006,an output section 1007, a storage section 1008, and a communicationsection 1009. The input section 1006 is made up of input devices such asa keyboard and a mouse with which the user inputs operation commands.The output section 1007 allows process operation screens or processresult images to appear on a display device. The storage section 1008 isgenerally formed by a hard disk drive for storing programs and variousdata. The communication section 1009 is typically composed of a LAN(local area network) adapter that performs communication processes overnetworks such as the Internet. Also, the input/output interface 1005 isconnected with a drive 1010 that writes and reads data to and from apiece of removable media 1011 such as magnetic disks (including flexibledisks), optical disks (including CD-ROM (Compact Disc-Read Only Memory)and DVD (Digital Versatile Disc)), magneto-optical disks (including MD(Mini Disc)), and semiconductor memories.

The CPU 1001 performs various processes in accordance with the programsstored in the ROM 1002 or in keeping with the programs that were readfrom the removable media 1011 such as the magnetic disk, optical disk,magneto-optical disk, or semiconductor memory and installed into thestorage section 1008 before being loaded from there into the RAM 1003.The RAM 1003 may also accommodate data necessary for the CPU 1001 toperform its diverse processing.

In this specification, the steps describing the programs stored on therecording storage medium represent not only the processes that are to becarried out in the depicted sequence (i.e., on a time series basis) butalso processes that may be performed parallelly or individually and notnecessarily chronologically.

The present application contains subject matter related to thatdisclosed in Japanese Priority Patent Application JP 2010-090608 filedin the Japan Patent Office on Apr. 9, 2010, the entire content of whichis hereby incorporated by reference.

It should be understood by those skilled in the art that variousmodifications, combinations, sub-combinations and alterations may occurdepending on design requirements and other factors insofar as they arewithin the scope of the appended claims or the equivalents thereof.

What is claimed is:
 1. An image processing apparatus comprising: acentral processing hardware unit including first layer summary datageneration means for, out of images extracted with a first frequencyfrom the images making up a moving image content, generating first layersummary data of a first size before registering said first layer summarydata to a database; second layer summary data generation means for, outof images extracted with a second frequency higher than said firstfrequency from the images making up said moving image content,generating second layer summary data of a second size smaller than saidfirst size; first search means for, based on said first layer summarydata generated by said first layer summary data generation means,searching said database for a corresponding moving image content; secondsearch means for, based on said second layer summary data generated bysaid second layer summary data generation means, searching the movingimage contents retrieved by said first search means for a correspondingmoving image content; and compression means for connecting differentregions between the moving image content retrieved by said second searchmeans on the one hand and the moving image content from which said firstlayer summary data is generated by said first layer summary datageneration means on the other hand, so as to delete either of thecontents for moving image content data compression.
 2. The imageprocessing apparatus according to claim 1, wherein said first layersummary data is made up of one or a combination of a pixel value, abrightness value, an activity, an audio volume, and an average ofamplitudes within a predetermined audio frequency band regarding each ofa plurality of partitioned regions constituting each of the images whichare part of said moving image contents and which are extracted therefromwith said first frequency, said first layer summary data having saidfirst size; and said second layer summary data is made up of at leastone or a combination of a pixel value, a brightness value, an activity,an audio volume, and an average of amplitudes within a predeterminedaudio frequency band regarding each of a plurality of partitionedregions constituting each of the images which are part of said movingimage contents and which are extracted therefrom with said secondfrequency higher than said first frequency, said second layer summarydata having said second size.
 3. The image processing apparatusaccording to claim 1, wherein said first and said second frequencies arethose with which the images are extracted from said moving imagecontents either periodically or nonperiodically.
 4. The image processingapparatus according to claim 3, wherein the periodical image extractionmeans extracting the images at intervals of a predetermined number offrames; and the nonperiodical image extraction means extracting theimages every time a scene change occurs in said moving image content orevery time a silent part of audio data is followed by a nonsilent partthereof.
 5. An image processing method for use with an image processingapparatus including first layer summary data generation means for, outof images extracted with a first frequency from the images making up amoving image content, generating first layer summary data of a firstsize before registering said first layer summary data to a database;second layer summary data generation means for, out of images extractedwith a second frequency higher than said first frequency from the imagesmaking up said moving image content, generating second layer summarydata of a second size smaller than said first size; first search meansfor, based on said first layer summary data generated by said firstlayer summary data generation means, searching said database for acorresponding moving image content; and second search means for, basedon said second layer summary data generated by said second layer summarydata generation means, searching the moving image contents retrieved bysaid first search means for a corresponding moving image content; saidimage processing method comprising the steps of: causing said firstlayer summary data generation means to generate, out of the imagesextracted with said first frequency from the images making up saidmoving image content, said first layer summary data of said first sizebefore registering said first layer summary data to said database;causing said second layer summary data generation means to generate, outof the images extracted with said second frequency higher than saidfirst frequency from the images making up said moving image content,said second layer summary data of said second size smaller than saidfirst size; causing said first search means to search said database forthe corresponding moving image content based on said first layer summarydata generated by said first layer summary data generation step; causingsaid second search means to search the moving image contents retrievedby said first search step for the corresponding moving image contentbased on said second layer summary data generated by said second layersummary data generation step; and connecting different regions betweenthe moving image content retrieved by said second search means on theone hand and the moving image content from which said first layersummary data is generated by said first layer summary data generationmeans on the other hand, so as to delete either of the contents formoving image content data compression.
 6. A program embodied on anon-transitory computer readable medium for use with a computercontrolling an image processing apparatus including first layer summarydata generation means for, out of images extracted with a firstfrequency from the images making up a moving image content, generatingfirst layer summary data of a first size before registering said firstlayer summary data to a database; second layer summary data generationmeans for, out of images extracted with a second frequency higher thansaid first frequency from the images making up said moving imagecontent, generating second layer summary data of a second size smallerthan said first size; first search means for, based on said first layersummary data generated by said first layer summary data generationmeans, searching said database for a corresponding moving image content;and second search means for, based on said second layer summary datagenerated by said second layer summary data generation means, searchingthe moving image contents retrieved by said first search means for acorresponding moving image content; said program causing said computerto execute a procedure comprising the steps of: causing said first layersummary data generation means to generate, out of the images extractedwith said first frequency from the images making up said moving imagecontent, said first layer summary data of said first size beforeregistering said first layer summary data to said database; causing saidsecond layer summary data generation means to generate, out of theimages extracted with said second frequency higher than said firstfrequency from the images making up said moving image content, saidsecond layer summary data of said second size smaller than said firstsize; causing said first search means to search said database for thecorresponding moving image content based on said first layer summarydata generated by said first layer summary data generation step; causingsaid second search means to search the moving image contents retrievedby said first search step for the corresponding moving image contentbased on said second layer summary data generated by said second layersummary data generation step; and connecting different regions betweenthe moving image content retrieved by said second search means on theone hand and the moving image content from which said first layersummary data is generated by said first layer summary data generationmeans on the other hand, so as to delete either of the contents formoving image content data compression.
 7. An image processing apparatuscomprising: a central processing circuitry including a first layersummary data generation section configured to, out of images extractedwith a first frequency from the images making up a moving image content,generate first layer summary data of a first size before registeringsaid first layer summary data to a database; a second layer summary datageneration section configured to, out of images extracted with a secondfrequency higher than said first frequency from the images making upsaid moving image content, generate second layer summary data of asecond size smaller than said first size; a first search sectionconfigured to, based on said first layer summary data generated by saidfirst layer summary data generation section, search said database for acorresponding moving image content; a second search section configuredto, based on said second layer summary data generated by said secondlayer summary data generation section, search the moving image contentsretrieved by said first search section configured to a correspondingmoving image content; and compression section configured to connectdifferent regions between the moving image content retrieved by saidsecond search section on the one hand and the moving image content fromwhich said first layer summary data is generated by said first layersummary data generation section on the other hand, so as to deleteeither of the contents for moving image content data compression.